RAPID Choices under Short-Term Threats and Behavioral Response to Social Distancing in the COVID-19 Pandemic

Social-interaction restrictions have to be respected to contain the spread of highly contagious diseases such as COVID-19. Ensuring compliance to strict health interventions, however, needs better understanding of individual decisions under risk. Until a vaccine is developed, policy makers not only need to find the best incentives for people to avoid physical proximity, but they also need to create plans for relaxing social distancing in the near future. Behavioral forecasts from the models developed in this project help to guide decisions in both cases. Specifically, this project provides answers to several policy-relevant questions such as: What changes in social behavior are people adopting in response to the disruptions created by the COVID-19 outbreak? Who is more likely to respect health guidelines, including social distancing protocols? What are safety/risk perceptions of physical proximity in public spaces (pharmacies, grocery stores, take-outs at restaurants) and how are these perceptions shaping daily choices? How to create incentives for individuals to sustain social-distancing? How are COVID-19 disruptions causing losses in individual welfare, and how can analysts derive metrics to value such losses?

In this project, the research team adopts and significantly adapts crowding research tools and methods in retail and transportation studies to analyze social distancing behaviors as preventive action against threats to health. An innovate virtual-reality-based online survey with choice experiments on social distancing collects time-sensitive behavioral data. The data are modeled using micro-econometric discrete-continuous choice models with structural equations for attitudinal components and heavy-tailed error distributions for preference shocks. Unlike standard thin-tailed distributions, error terms that exhibit heavy tails not only generalize standard assumptions but also address decision-uncertainty behavior. Flexible decision rules under risk are integrated into the discrete-continuous choice models that represent time-use scheduling during total and partial lock-downs. Ultimately, research outcomes from this study provide guidance to policy-makers for how to best implement measures such as social distancing and quarantines in order to control major epidemics, and then how to best phase out these measures.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.